
It is the first time that the agent-environment interface is investigated for developing reinforcement learning algorithm.

We prove that the surrogate probability agent-environment interface gives the optimal policy solution to the task interface.

We develop the probability surrogate action deterministic policy gradient (PSADPG) algorithm based on SAEI which validate the surrogate agent-environment interface framework on algorithm development.

PSADPG achieves the performance of DQN in certain tasks with the stochastic optimal policy nature in the initial training stage.

PSADPG enables DQN [1]} style off-policy learning algorithms (such as Double DQN [2]}, Dueling DQN [3]}, Prioritized DQN [4]}) for stochastic discrete control. PSADPG augments the spectrum of deep reinforcement learning algorithm with extra dimensions.

